---
title: "Mario Kart Time Trial PBs 150cc"
output:
  flexdashboard::flex_dashboard:
    orientation: columns
    theme: sandstone
    navbar:
      - { title: "Speedruns", href: "sr.html", align: right}
      - { icon: "fas fa-home", href: "index.html", align: right}
    favicon: blueshell.png
    source_code: embed
---

<script>
$(document).ready(function(){
    $('[data-toggle="popover"]').popover(); 
});
</script>

```{r lib}
library(tidyverse)
library(rvest)
library(plotly)
library(lubridate)
library(knitr)
library(kableExtra)
```

```{r scrapeWRs}
html <- read_html("http://www.mkwrs.com/mk8dx/wrs.php")

ttwr0 <- html %>% html_elements(".wr") %>% html_table() %>% .[[1]]
```


```{r import}
abr <- read_csv("_data/track-abbr.csv")

tt <- read_csv("_data/time-trials.csv",
               col_types = cols(total = "c")) %>%
  filter(cc == 150) %>%
  mutate(
    total = ms(total),
    yr = year(date),
    mth = month(date),
    day = day(date),
    hour = hour(time),
    min = minute(time),
    dt = make_datetime(
      year = yr,
      month = mth,
      day = day,
      hour = hour,
      min = min
    )
  ) %>%
  left_join(abr)

ttwr <- ttwr0 %>% 
  select(track = Track, total = `Time+Video`) %>% 
  filter(track != "Total:") %>% 
  distinct() %>% 
  mutate(total = str_replace_all(total, "'", ":"),
         total = str_replace_all(total, "\"", "."),
         total = ms(total)) %>% 
  left_join(abr, by = c("track" = "lss")) %>% 
  select(trk, total)
```


```{r makeVars}
ttDiff <- tt %>% 
  arrange(trk, dt) %>% 
  group_by(trk) %>% 
  mutate(prev = lag(total), .after = total,
         diff = as.double(prev - total, units = "secs"),
         diff = replace_na(diff, 0)
         ) %>% 
  mutate(cumsum = cumsum(diff), .after = diff) 

wk <- ttDiff %>% 
  filter(!is.na(prev)) %>% 
  ungroup() %>% 
  mutate(week = (week(date)-4)) %>% 
  group_by(week) %>% 
  summarise(mean = mean(diff), n = n()) %>% 
  add_row(week=16, mean=0, n=0, .after = 15)

wr <- ttDiff %>% 
  slice_max(dt) %>% 
  ungroup() %>% 
  arrange(trkNO) %>% 
  left_join(ttwr, by = "trk") %>% 
  select(date, track, total.x, cumsum, cup, type, total.y) %>% 
  mutate(diff = as.double(total.x - total.y, units = "secs"))
```

# Tracks 1-48 

## Column 1 tbl {data-width="320"}

### Worst Tracks

```{r worst48}
wr %>% 
  filter(!str_starts(type, "wave")) %>% 
  select(track, diff) %>% 
  arrange(desc(diff)) %>% 
  head(7) %>% 
  kable(
    col.names = c('track' = 'Track',
    'diff' = 'WR Diff.')
  ) %>% 
  kable_styling()
```

### Best Tracks

```{r best48}
wr %>% 
  filter(!str_starts(type, "wave")) %>% 
  select(track, diff) %>% 
  arrange(diff) %>% 
  head(7) %>% 
  kable(
    col.names = c('track' = 'Track',
    'diff' = 'WR Diff.')
  ) %>% 
  kable_styling()
```

### Most Improved since Feb. 2022

```{r mostImprv48}
wr %>% 
  filter(!str_starts(type, "wave")) %>% 
  select(track, cumsum) %>% 
  arrange(desc(cumsum)) %>% 
  head(7) %>% 
  kable(
    col.names = c('track' = 'Track',
    'cumsum' = 'Improvement')
  ) %>% 
  kable_styling()
```

## Column 2

### PBs vs WRs

```{r}
wr2 <- wr %>%
  filter(!str_starts(type, "wave")) %>% 
  select(cup, track, date, total.x, total.y, diff) 

wr2 %>%
  kbl(
    col.names = c(" ", "Track", "PB Date", "PB", "WR", "WR Diff"),
    align = "c",
    escape = FALSE
  ) %>%
  kable_styling(full_width = TRUE) %>%
  column_spec(column = 1,
              extra_css = 'transform: rotate(270deg);') %>%
  column_spec(3:4,
              extra_css = 'font-size: 80%;') %>%
  column_spec(
    6,
    color = "white",
    background = spec_color(
      wr2$diff,
      begin = 0.3,
      end = 0.7,
      alpha = 0.7,
      option = "A"
    ),
    popover = paste0("WR: ", wr2$total.y)
  ) %>%
  remove_column(5) %>%
  collapse_rows(columns = 1,
                row_group_label_position = 'stack') %>%
  row_spec(0, align = "c")
```

## Column 2 violin

### PB Time Distributions, Feb 01, 2022 - Present

```{r violin48, fig.height=10}
tt48 <- tt %>% 
  filter(trkNO <= 48) %>%
  mutate(track = fct_reorder(track, trkNO))

ggtt <- tt48 %>% 
  filter(trk != "dBP") %>% 
  group_by(track) %>% 
  mutate(sd = sd(total)) %>% 
  ungroup() %>% 
  ggplot(aes(factor(track), total)) +
  geom_violin(draw_quantiles = 0.5, 
              scale = "width",
              aes(fill = sd, color=sd),
              alpha = .7) + 
  stat_summary(fun = "median", geom = "point", size = .4) +
  scale_fill_gradient(low = "darkcyan", high = "plum") +
  scale_color_gradient(low = "darkcyan", high = "plum") +
  scale_y_time() +
  scale_x_discrete(limits = rev(levels(tt48$track))) +
  coord_flip() +
  labs(x = "",
       y = "PB Time") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45,
                                   hjust = 0.98,
                                   vjust = 0.9),
        axis.text = element_text(size = 8),
        axis.ticks = element_line(size = .2),
        panel.grid = element_line(size = .2),
        panel.border = element_rect(fill = NA, size = .2),
        legend.position = "none")

ggplotly(ggtt)
```

# Tracks 49-96 

## Column 1 tbl {data-width="320"}

### Worst Tracks

```{r worst96}
wr %>% 
  filter(str_starts(type, "wave")) %>% 
  select(track, diff) %>% 
  arrange(desc(diff)) %>% 
  head(7) %>% 
  kable(
    col.names = c('track' = 'Track',
    'diff' = 'WR Diff.')
  ) %>% 
  kable_styling()
```

### Best Tracks

```{r best96}
wr %>% 
  filter(str_starts(type, "wave")) %>% 
  select(track, diff) %>% 
  arrange(diff) %>% 
  head(7) %>% 
  kable(
    col.names = c('track' = 'Track',
    'diff' = 'WR Diff.')
  ) %>% 
  kable_styling()
```

### Most Improved 

```{r mostImprv96}
wr %>% 
  filter(str_starts(type, "wave")) %>% 
  select(track, cumsum) %>% 
  arrange(desc(cumsum)) %>% 
  head(7) %>% 
  kable(
    col.names = c('track' = 'Track',
    'cumsum' = 'Improvement')
  ) %>% 
  kable_styling()
```

## Column 2 tbl

### PBs vs WRs

```{r}
PBw <- wr %>%
  filter(str_starts(type, "wave")) %>%
  select(cup, track, date, total.x, total.y, diff)

PBw %>%
  kbl(
    col.names = c(" ", "Track", "PB Date", "PB", "WR", "WR Diff"),
    align = "c"
  ) %>%
  kable_styling(full_width = TRUE) %>%
  column_spec(column = 1,
              extra_css = 'transform: rotate(270deg);') %>%
  column_spec(3:4,
              extra_css = 'font-size: 80%;') %>%
  column_spec(
    6,
    color = "white",
    background = spec_color(
      PBw$diff,
      begin = 0.3,
      end = 0.7,
      alpha = 0.7,
      option = "A"
    ),
    popover = paste0("WR: ", PBw$total.y)
  ) %>%
  remove_column(5) %>%
  collapse_rows(columns = 1,
                row_group_label_position = 'stack') %>%
  row_spec(0, align = "c")
```

## Column 3 violin

### PB Time Distributions, March 18, 2022 - Present

```{r fig.height=10}
tt96 <- tt %>% 
  filter(trkNO > 48) %>%
  mutate(track = fct_reorder(track, trkNO))

gg96tt <- tt96 %>% 
  group_by(track) %>% 
  mutate(sd = sd(total)) %>% 
  ungroup() %>% 
  ggplot(aes(factor(track), total)) +
  geom_violin(draw_quantiles = 0.5, 
              scale = "width",
              aes(fill = sd, color=sd),
              alpha = .7) + 
  stat_summary(fun = "median", geom = "point", size = .4) +
  scale_y_time() +
  scale_fill_gradient(low = "darkcyan", high = "plum") +
  scale_color_gradient(low = "darkcyan", high = "plum") +
  scale_x_discrete(limits = rev(levels(tt96$track))) +
  coord_flip() +
  labs(x = "",
       y = "PB Time") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45,
                                   hjust = 0.98,
                                   vjust = 0.9),
        axis.text = element_text(size = 8),
        axis.ticks = element_line(size = .2),
        panel.grid = element_line(size = .2),
        panel.border = element_rect(fill = NA, size = .2),
        legend.position = "none")

ggplotly(gg96tt)
```

# Overall

## Column 1

### Number of PBs by Week

```{r ggPBwk}
ggN <- wk %>% 
  ggplot() +
  geom_line(aes(x=week, y=n)) +
  theme_minimal() +
  labs(y = "PBs",
       x = "Week")

ggplotly(ggN)
```

### Average Improvement by Week

```{r ggAvgImprv}
ggWk <- wk %>% 
  ggplot(aes(x=week, y=mean)) +
  geom_line() +
  theme_minimal() + 
  labs(x = "Week",
       y = "Average Time Save (secs)")

ggplotly(ggWk)
```

### Cumulative Time Save

```{r}
CTS <- ttDiff %>% 
  ungroup() %>% 
  filter(!is.na(prev)) %>% 
  arrange(dt) %>% 
  mutate(cumsum = cumsum(diff))
  
ggCTS <- CTS %>% 
  ggplot(aes(x=dt, y=cumsum)) +
  geom_step() +
  theme_minimal() +
  labs(x="", y="Cumulative Time Saved (secs)") +
  theme(axis.text.x = element_text(angle = 90),
        legend.position = "none")

ggplotly(ggCTS)
```

## Column 2 {data-width="420"}

### Oldest PBs

```{r old}
wr %>% 
  select(track, date) %>% 
  arrange(date) %>% 
  head(7) %>% 
  kable(
    col.names = c('track' = 'Track',
    'date' = 'PB Date')
  ) %>% 
  kable_styling()
```

### Largest PBs

```{r}
ttDiff %>%
  ungroup() %>% 
  select(track, date, diff) %>% 
  arrange(desc(diff)) %>% 
  head(7) %>% 
  kable(
    col.names = c(
      'track' = 'Track',
      'date' = 'Date',
      'diff' = 'Improvement')
  ) %>% 
  kable_styling()
```

### Smallest PBs

```{r}
ttDiff %>%
  ungroup() %>% 
  select(track, date, diff) %>% 
  filter(diff != 0) %>% 
  arrange(diff) %>% 
  head(7) %>% 
  kable(
    col.names = c(
      'track' = 'Track',
      'date' = 'Date',
      'diff' = 'Improvement')
  ) %>% 
  kable_styling()
```

## Column 3 {data-width="420"}

### Newest PBs

```{r newest}
ttDiff %>%
  ungroup() %>% 
  select(track, date, diff, dt) %>% 
  slice_max(dt, n=50) %>% 
  select(-dt) %>% 
  kable(
    col.names = c(
      'track' = 'Track',
      'date' = 'Date',
      'diff' = 'Improvement')
  ) %>% 
  kable_styling()
```